change: shorten AutoML test run (aws#521)

Author: icywang86rui

tests/integ/test_auto_ml.py

@@ -57,7 +57,7 @@ def test_auto_ml_fit(sagemaker_session):
         role=ROLE,
         target_attribute_name=TARGET_ATTRIBUTE_NAME,
         sagemaker_session=sagemaker_session,
-        max_candidates=3,
+        max_candidates=1,
     )
 
     job_name = unique_name_from_base("auto-ml", max_length=32)
@@ -76,6 +76,7 @@ def test_auto_ml_fit_local_input(sagemaker_session):
         target_attribute_name=TARGET_ATTRIBUTE_NAME,
         sagemaker_session=sagemaker_session,
         max_candidates=1,
+        generate_candidate_definitions_only=True,
     )
 
     inputs = TRAINING_DATA
@@ -94,6 +95,7 @@ def test_auto_ml_input_object_fit(sagemaker_session):
         target_attribute_name=TARGET_ATTRIBUTE_NAME,
         sagemaker_session=sagemaker_session,
         max_candidates=1,
+        generate_candidate_definitions_only=True,
     )
     job_name = unique_name_from_base("auto-ml", max_length=32)
     s3_input = sagemaker_session.upload_data(path=TRAINING_DATA, key_prefix=PREFIX + "/input")
@@ -118,6 +120,7 @@ def test_auto_ml_fit_optional_args(sagemaker_session):
         output_path=output_path,
         problem_type=problem_type,
         job_objective=job_objective,
+        generate_candidate_definitions_only=True,
     )
     inputs = TRAINING_DATA
     with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
@@ -281,38 +284,6 @@ def test_deploy_best_candidate(sagemaker_session, cpu_instance_type):
     sagemaker_session.sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
 
 
-@pytest.mark.skipif(
-    tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
-    reason="AutoML is not supported in the region yet.",
-)
-def test_create_model_best_candidate(sagemaker_session, cpu_instance_type):
-    auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
-
-    auto_ml = AutoML.attach(auto_ml_job_name=AUTO_ML_JOB_NAME, sagemaker_session=sagemaker_session)
-    best_candidate = auto_ml.best_candidate()
-
-    with timeout(minutes=5):
-        pipeline_model = auto_ml.create_model(
-            name=DEFAULT_MODEL_NAME,
-            candidate=best_candidate,
-            sagemaker_session=sagemaker_session,
-            vpc_config=None,
-            enable_network_isolation=False,
-            model_kms_key=None,
-            predictor_cls=None,
-        )
-    inputs = sagemaker_session.upload_data(
-        path=TRANSFORM_DATA, key_prefix=PREFIX + "/transform_input"
-    )
-    pipeline_model.transformer(
-        instance_count=1,
-        instance_type=cpu_instance_type,
-        assemble_with="Line",
-        output_path="s3://{}/{}".format(sagemaker_session.default_bucket(), "transform_test"),
-        accept="text/csv",
-    ).transform(data=inputs, content_type="text/csv", split_type="Line", join_source="Input")
-
-
 @pytest.mark.skipif(
     tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
     reason="AutoML is not supported in the region yet.",
@@ -346,39 +317,6 @@ def test_candidate_estimator_default_rerun_and_deploy(sagemaker_session, cpu_ins
     sagemaker_session.sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
 
 
-@pytest.mark.skipif(
-    tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
-    reason="AutoML is not supported in the region yet.",
-)
-def test_candidate_estimator_rerun_with_optional_args(sagemaker_session, cpu_instance_type):
-    auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
-
-    auto_ml = AutoML(
-        role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
-    )
-
-    candidates = auto_ml.list_candidates(job_name=AUTO_ML_JOB_NAME)
-    candidate = candidates[1]
-
-    candidate_estimator = CandidateEstimator(candidate, sagemaker_session)
-    inputs = sagemaker_session.upload_data(path=TEST_DATA, key_prefix=PREFIX + "/input")
-    endpoint_name = unique_name_from_base("sagemaker-auto-ml-rerun-candidate-test")
-    with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
-        candidate_estimator.fit(inputs, encrypt_inter_container_traffic=True)
-        auto_ml.deploy(
-            initial_instance_count=INSTANCE_COUNT,
-            instance_type=cpu_instance_type,
-            candidate=candidate,
-            endpoint_name=endpoint_name,
-        )
-
-    endpoint_status = sagemaker_session.sagemaker_client.describe_endpoint(
-        EndpointName=endpoint_name
-    )["EndpointStatus"]
-    assert endpoint_status == "InService"
-    sagemaker_session.sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
-
-
 @pytest.mark.skipif(
     tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
     reason="AutoML is not supported in the region yet.",